ebayScraper VS tidytuesday

If you ever want to expand on this project more, you might enjoy looking at my implementation of an eBay Scraper I made last year: https://github.com/driscoll42/ebayMarketAnalyzer You can see the code I used to specify a specific search to scrape eBay for those instead of needing to put the specific search URL, also filters based on price. The main issue you'll run into sooner or later are CAPTCHAs eBay added earlier this year.

Except they're not, I speak from personal experience. I built a scraper for eBay to analyze sales data and a few months ago eBay added CATPCHAs to the site which prevented my tool from working. They were more complex than the one OP is working on, but still CAPTCHAs. Further I got several emails from other eBay scrapers asking me if I was working on a solution around it. CAPTCHAs aren't perfect but they do work to prevent a large segment of people from scraping a site. If eBay had had CAPTCHAs from the beginning my project never would have started at all.

Here's the repo! https://github.com/driscoll42/ebayScraper I'd love any suggestions to improve. It makes sense on the background/thicker lines, though the image size is a Tom's Hardware thing, by default they're much larger. Example

Source Code for Data Scraping: https://github.com/driscoll42/ebayScraper

No but I'll check that out. I just wrote a pythong script, primarily calling a url with requests and then using beautifulsoup to parse the data. Here's a link if you want to look at it: https://github.com/driscoll42/ebayScraper

Source Code: https://github.com/driscoll42/ebayScraper

TidyTuesday is a weekly data cleaning project where a new, interesting data source is linked to each week: https://github.com/rfordatascience/tidytuesday

For R, I don't have specific resources, but I remember I started out with doing tidytuesdays challenge (https://github.com/rfordatascience/tidytuesday).

Tidy Tuesday has data and links to more data. The nice thing about those data sets is that you can search for what other people did with the data on social media (e.g. Twitter).

Dataset: https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-11-01

What you're describing is contributing to open source. Some things I'd suggest doing: - learn some git first - create GitHub account and create at least a practice repo - look at learning community-related repos, like Tidy Tuesday - follow R "power" users, people associated with RStudio, and similar folks on social media. Those folks will sometimes mention projects aimed at beginners.

Data: TidyTuesday April 4

The Tidy Tuesday git repo has a lot of example datasets to work with.

I mostly use ggplot2 in R for visualisations which means that The R Graph Gallery is my starting point for inspiration. The best thing to do is start with a simple idea that tells a story, and one of the best guys out there that does this is Cedric Scherer. He is involved a bit with the TidyTuesday project which I wish I had more time to play around with, and is a great starting point for developing a library of vis techniques.